---
id: ropt-dakota
version: "0.28.0"
license: unclear
license_treatment: copyleft
maintenance: active
---

# ropt-dakota — A Dakota optimizer plugin for ropt

License: copyleft · Maintenance: active · Downloads: 95.3K/mo

## What it is and what it does

ropt-dakota is a plugin that bridges the ropt robust optimization framework with Dakota, an open-source optimization package from Sandia. It lets you use Dakota's algorithms—gradient-based, derivative-free, and stochastic methods—within ropt's workflow and API. The plugin is maintained by TNO (Netherlands Organisation for Applied Scientific Research) and depends on numpy for numerical work and carolina as a Python wrapper around Dakota.

The package is designed for scientific and engineering optimization tasks where you want to leverage Dakota's solver suite without reimplementing ropt's robust optimization logic. It supports Python 3.11 through 3.14 and installs as a pure Python wheel with minimal friction.

Use it for:

- Run Dakota optimization algorithms within ropt's robust optimization framework for parameter estimation.
- Access Dakota's derivative-free solvers for black-box optimization problems where gradients are unavailable.
- Combine Dakota's stochastic methods with ropt's uncertainty quantification for robust design optimization.
- Integrate Dakota algorithms into existing ropt-based workflows without rewriting solver interfaces.
- Benchmark Dakota solvers against other ropt plugins on the same optimization problem.

## Worth the install?

AI-flagged interpretation of the facts above — verify before relying.

Provides Dakota optimization algorithms as a plugin for the ropt robust optimization framework, enabling access to Dakota's solvers within ropt workflows.

Yes, if you already use ropt and need Dakota's algorithms. The package is actively maintained, has no known vulnerabilities, and installs cleanly. The copyleft license (GPLv3) is a consideration for proprietary projects. Its niche audience reflects the specialized nature of robust optimization rather than any quality concern.

## Install

pip install ropt-dakota
uv add ropt-dakota
poetry add ropt-dakota

## Installing ropt-dakota

Before you install: Low friction install with a pure-Python wheel. Actively maintained as of 2026-08-13 with recent releases. Requires Python 3.11 or later and depends on numpy and carolina.

License in practice: Released under GPLv3 (copyleft). Users must comply with copyleft obligations if they distribute derivative works or link this into proprietary software.

Quickstart:

pip install ropt-dakota

from ropt import Optimizer
from ropt_dakota import DakotaPlugin

optimizer = Optimizer(plugin=DakotaPlugin())

Requires Python 3.11 or later; Dakota optimizer must be available in the environment (typically via carolina dependency).

Verify before relying:

- Whether Dakota itself must be separately installed or is bundled via carolina
- Performance characteristics and scalability limits for typical optimization problems
- Specific algorithm coverage compared to direct Dakota use

## Package facts

- License: not declared (copyleft)
- Python support: supports_current
- Install friction: low
- Maintenance: active
- Downloads: 95.3K/month (top 15,000 on PyPI)
- Known vulnerabilities: none known

## Tags

dakota optimizer plugin, ropt robust optimization, dakota algorithm wrapper, scientific optimization framework, robust optimization plugin, dakota python interface, optimization algorithm access, optimization, robust-design, scientific-computing

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